CN107591840A - A kind of more micro-grid system reliability estimation methods in region for considering random correlation - Google Patents
A kind of more micro-grid system reliability estimation methods in region for considering random correlation Download PDFInfo
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Abstract
The invention discloses a kind of more micro-grid system reliability estimation methods in region for considering random correlation, it comprises the following steps：Step 1, the random correlation running variable to microgrid group is analyzed；Step 2, wind speed in microgrid group and load are carried out establishing microgrid group's reliability model；Step 3, variable is run to microgrid group to be sampled；Step 4, the convergence situation of microgrid group's reliability index is judged；Step 5, the power supply strategy in microgrid group's reliability assessment is formulated；Step 6, the reliability of microgrid group's system is calculated.The present invention considers the random correlation run in microgrid group between each microgrid between variable；And microgrid group Reliability Evaluation Model is applied to microgrid group's Reliability Evaluation Algorithm, to provide guidance for the power supply of microgrid group high reliability, not only microgrid group coordinated operation scheduling provides guidance, and ensure that microgrid group high reliability is powered.
Description
Technical field
The present invention relates to Model in Reliability Evaluation of Power Systems technical field, specifically a kind of area for considering random correlation
The more micro-grid system reliability estimation methods in domain.
Background technology
In recent years, with the development of micro-capacitance sensor technology, some micro-capacitance sensor engineerings have been built in various regions successively.In industrial park etc.
Occasion, the microgrid group being made up of some micro-capacitance sensors have become one of trend of micro-capacitance sensor development field.
The more complicated system that microgrid group is made up of some micro-capacitance sensors, there is the characteristics of different compared to micro-capacitance sensor, such as
There is the mutual Ji of energy and support behavior between each microgrid in microgrid group's system.But microgrid faciation compared with single micro-capacitance sensor and
Speech power supply reliability has more complicated feature.
Therefore, it is necessary to seek a kind of evaluation scheme that can assess microgrid group's system power supply reliability, assisted for microgrid group
Traffic control is adjusted to provide guidance.
The content of the invention
In view of the shortcomings of the prior art, it is reliable to propose a kind of more micro-grid systems in region for considering random correlation by the present invention
Property appraisal procedure, it can dispatch for microgrid group coordinated operation and provide guidance, ensure that microgrid group high reliability is powered.
The present invention solves its technical problem and adopted the technical scheme that：
A kind of more micro-grid system reliability estimation methods in region for considering random correlation provided in an embodiment of the present invention, can
To comprise the following steps：
Step 1, the random correlation running variable to microgrid group is analyzed；
Step 2, wind speed in microgrid group and load are carried out establishing microgrid group's reliability model；
Step 3, variable is run to microgrid group to be sampled；
Step 4, the convergence situation of microgrid group's reliability index is judged；
Step 5, the power supply strategy in microgrid group's reliability assessment is formulated；
Step 6, the reliability of microgrid group's system is calculated.
As a kind of possible implementation of the present embodiment, in step 1, for n variable xi, using hypothesis testing
Method is analyzed the random correlation variable, and detailed process comprises the following steps：
Step 11, H is assumed in definition0With alternative hypothesis H1, it is assumed that H0For n variable xiBetween uncorrelated, i.e. correlation coefficient ρ
=0；Alternative hypothesis H1For n variable xiBetween related, i.e. correlation coefficient ρ ≠ 0, index of correlation α=1；
Step 12, the statistic γ between variable is calculatedsAnd by with table look-up to obtain its critical statistics amount
Step 13, determine whether to receive to assume H0：IfThen receive to assume H0, otherwise refuse to assume H0, receive
Alternative hypothesis H1。
As a kind of possible implementation of the present embodiment, in step 2, using Vine copula function pair microgrid groups
Interior wind speed and load carry out establishing microgrid group's reliability model, and detailed process comprises the following steps：
Step 21, n dimension variable datas x is asked for respectively1, x2, x3..., xnProbability density function and cumulative probability density letter
Number, and f is designated as respectivelyi(xi) and Fi(xi)；
Step 22, variable data x is asked for respectively1, x2, x3..., xnBetween Spearsman relative coefficients, and be designated as ρij；
Step 23, to relative coefficient ρijRelevance ranking is carried out according to magnitude relationship；
Step 24, the ranking results based on coefficient correlation, variable is carried out according to the modeling rule of Vine copula functions
Modeling, obtain n dimension variables x1, x2, x3..., xnRandom relevance function expression formula.
As a kind of possible implementation of the present embodiment, the random relevance function expression formula includes：
The probability density function of wind speed and the expression formula of cumulative probability density function are respectively in microgrid group：
F (v)=(k/c) (v/c)k-1·exp(-(v/c)k) (1)
F (v)=1-exp (- (v/c)k) (2)
In formula：V is wind speed, and k and c are respectively form parameter and scale parameter；
The probability density function of microgrid group's internal loading and the expression formula of cumulative probability density function are respectively：
In formula：L is load, and μ and σ are respectively the mathematic expectaion and mean square deviation of Gaussian Profile；
N dimension Vine copula functions basic structure be：
In formula：xiFor i-th of variable；For variable xn-1、xnCopula functions, v-jFor except variable xjOuter variable
Set.
It is in step 3, described that microgrid group operation variable is sampled as a kind of possible implementation of the present embodiment
Process comprise the following steps：
Step 31, uniform random number w is produced in section [0,1]1、w2、w3；
Step 32, x is made1=w1；By w2Bring formula x into2=f-1(w2,x1,ρ1), obtain x2；
Step 33, by w2、w3Bring formula x into3=f-1[f-1(w3,x2,ρ3),x1,p2], obtain x3；
Step 34, (x1, x2, x3) it is the random number for meeting to require；
As a kind of possible implementation of the present embodiment, in step 3, for the size of intensity of illumination, then by taking out
Sample atmospheric transparency coefficient kt, and then obtain the sample value of intensity of illumination：
ρ1Respectively variable x1With x2Between coefficient correlation；ρ3For variable x2With x3Between coefficient correlation；k、ktuRespectively treat
Sampling atmospheric transparency coefficient and atmospheric transparency coefficient maximum；C is the form factor and scale coefficient of Weibull distribution.
As a kind of possible implementation of the present embodiment, in step 4, sentenced using the convergence of scarce power supply figureofmerit
The convergence situation of disconnected microgrid group's reliability index, basis for estimation are shown below：
β, E (x), ε are respectively convergence parameter, variable x average and convergence.
As a kind of possible implementation of the present embodiment, in steps of 5, the power supply in the microgrid group reliability assessment
Strategy includes：
Meet the power demands of critical load in this microgrid first, and calculate microgrid residue power supply capacity；
If microgrid has remaining power supply capacity, the power demands for meeting critical load in other microgrids are considered；
Meet the power demands of non-key load in this microgrid again；
Finally meet the power demands of non-key load in other microgrids.
As a kind of possible implementation of the present embodiment, in step 6, the reliability for calculating microgrid group's system
Process comprises the following steps：
Step 61, history meteorological data, component reliability parameter, microgrid group configuration data and distribution where microgrid group are inputted
Web frame etc. calculates data；
Step 62, the Spearsman relevance parameters of calculation of wind speed historical data and demand history data, and establish correspondingly
Vine Copula functions；Establish the probability-distribution function of the atmospheric transparency data obtained by history intensity of illumination data；
Step 63, sample microgrid group's running status, based on foundation Vine Copula function models sampling microgrid group in point
The running status of cloth power supply and load；
Step 64, microgrid group's self-energy allocation strategy is determined, calculates each microgrid inscribe load；
Step 65, microgrid group's power supply reliability index, and the convergence of judge index are calculated；Sentence as index reaches convergence
According to then performing next step；Otherwise step 63 is performed；
Step 66, result of calculation is exported.
The technical scheme of the embodiment of the present invention can have the advantage that as follows：
A kind of more micro-grid system reliability assessment sides in region of the random correlation of consideration of technical scheme of the embodiment of the present invention
Method mainly includes the following steps that：Random correlation running variable to microgrid group is analyzed；To wind speed in microgrid group and bear
Lotus carries out establishing microgrid group's reliability model；Variable is run to microgrid group to be sampled；Judge the receipts of microgrid group's reliability index
Hold back situation；Formulate the power supply strategy in microgrid group's reliability assessment；Calculate the reliability of microgrid group's system.Skill of the embodiment of the present invention
Advantage of the art scheme based on Vine Copula functions in terms of random correlation between describing multiple variables, to each in microgrid group
Load data in microgrid inner blower installation site air speed data and each microgrid establishes corresponding model respectively, and is based on
Vine Copula functions establish microgrid group's Reliability Evaluation Algorithm；Consider in microgrid group run between each microgrid variable it
Between random correlation；And microgrid group Reliability Evaluation Model is applied to microgrid group's Reliability Evaluation Algorithm, to be micro-
Net the power supply of group's high reliability and guidance is provided, only microgrid group coordinated operation scheduling does not provide guidance, and ensure that microgrid group
High reliability is powered.
Compared with prior art, technical scheme of the embodiment of the present invention has the characteristics that：
1) each microgrid position wind speed and load data in the microgrid group based on Vine copula functions are established
Random correlation models, the model of foundation preferably reflect the practical operation situation of microgrid group's inner blower；
2) the microgrid group's Reliability evaluation algorithm for considering random correlation is established, the assessment algorithm considers microgrid
The difference that group's internal loading requires for power supply reliability.By carrying out model to microgrid group's self-energy allocation strategy, fully excavate
The power supply potential of microgrid group, laid the foundation to formulate corresponding operation reserve and Optimal Decision-making.
Brief description of the drawings
Fig. 1 is a kind of more micro-grid system reliabilities in region of the random correlation of consideration according to an exemplary embodiment
The flow chart of appraisal procedure；
Fig. 2 is a kind of flow chart of the reliability of calculating microgrid group's system according to an exemplary embodiment；
Fig. 3 is the system construction drawing of microgrid group according to an exemplary embodiment a kind of.
Embodiment
For the technical characterstic for illustrating this programme can be understood, below by embodiment, and its accompanying drawing is combined, to this hair
It is bright to be described in detail.Following disclosure provides many different embodiments or example is used for realizing the different knots of the present invention
Structure.In order to simplify disclosure of the invention, hereinafter the part and setting of specific examples are described.In addition, the present invention can be with
Repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not indicated
Relation between various embodiments are discussed and/or set.It should be noted that part illustrated in the accompanying drawings is not necessarily to scale
Draw.Present invention omits the description to known assemblies and treatment technology and process to avoid being unnecessarily limiting the present invention.
Often be configured with blower fan, photovoltaic distributed generator unit in microgrid, and in microgrid group each microgrid in geographical position
Put and often close on.By geographical microenvironment, the wind speed on each microgrid inner blower position often and differs, and shows
Go out certain otherness.For this reason, it may be necessary to preferably described for the operation characteristic of microgrid group's inner blower with establishing the model of correlation
The operation characteristic of each blower fan position in microgrid group.Photovoltaic module is important generator unit in microgrid.Intensity of illumination becomes
Amount has influence on the output of photovoltaic in microgrid, and intensity of illumination variable is mainly influenceed by latitude, and geographical microenvironment is to intensity of illumination
Influence little.Therefore, an intensity of illumination variate model can be established for each photovoltaic in microgrid group.
In microgrid group's system, the load composition of each microgrid is often not quite similar.Its change of the load of each type
Rule shows certain uniqueness.At the same time, influenceed by people's production and habits and customs, the variation characteristic of each type load
There is also certain similitude.Therefore, there is certain random phase for the variation characteristic of each microgrid internal loading in microgrid group
Guan Xing.
The random correlative character of microgrid group's inner blower position wind speed and load can have influence on the operation of microgrid group
State, and then have influence on the power supply reliability of microgrid group.For this reason, it may be necessary to paid attention in microgrid group's reliability assessment.In number
In theory, random correlation models of the generally use based on Vine copula functions describe the random correlation between multiple variables
Property.During Vine copula functions are incorporated into microgrid group Reliability modeling and assessed by the present invention, to preferably assess microgrid group
Operational reliability.
Fig. 1 is a kind of more micro-grid system reliabilities in region of the random correlation of consideration according to an exemplary embodiment
The flow chart of appraisal procedure.A kind of as shown in figure 1, more microgrid systems in region for considering random correlation provided in an embodiment of the present invention
System reliability estimation method, may comprise steps of：
Step 1, the random correlation running variable to microgrid group is analyzed；
Step 2, wind speed in microgrid group and load are carried out establishing microgrid group's reliability model；
Step 3, variable is run to microgrid group to be sampled；
Step 4, the convergence situation of microgrid group's reliability index is judged；
Step 5, the power supply strategy in microgrid group's reliability assessment is formulated；
Step 6, the reliability of microgrid group's system is calculated.
In a kind of possible implementation, because for n variable x i, the method for generally use hypothesis testing is to becoming
Random correlation between amount is analyzed.Therefore, in step 1, using the method for hypothesis testing the random correlation variable
The detailed process that property is analyzed comprises the following steps：
Step 11, H is assumed in definition0With alternative hypothesis H1, it is assumed that H0For n variable xiBetween uncorrelated, i.e. correlation coefficient ρ
=0；Alternative hypothesis H1For n variable xiBetween related, i.e. correlation coefficient ρ ≠ 0, index of correlation α=1；
Step 12, the statistic γ between variable is calculatedsAnd by with table look-up to obtain its critical statistics amount
Step 13, determine whether to receive to assume H0：IfThen receive to assume H0, otherwise refuse to assume H0, receive
Alternative hypothesis H1。
In a kind of possible implementation, in step 2, using wind speed in Vine copula function pair microgrid groups and
Load carries out establishing microgrid group's reliability model, and its detailed process comprises the following steps：
Step 21, n dimension variable datas x is asked for respectively1, x2, x3..., xnProbability density function and cumulative probability density letter
Number, and f is designated as respectivelyi(xi) and Fi(xi)；
Step 22, variable data x is asked for respectively1, x2, x3..., xnBetween Spearsman relative coefficients, and be designated as ρij；
Step 23, to relative coefficient ρijRelevance ranking is carried out according to magnitude relationship；
Step 24, the ranking results based on coefficient correlation, variable is carried out according to the modeling rule of Vine copula functions
Modeling, obtain n dimension variables x1, x2, x3..., xnRandom relevance function expression formula.
In microgrid based on Vine copula functions in the random correlation modeling of wind speed, load, by by the general of variable
Rate is distributed and cumulative probability distribution is handled according to certain rule, obtains describing the random correlation of multiple variables
Joint probability density function.
In a kind of possible implementation, the random relevance function expression formula includes：
The probability density function of wind speed and the expression formula of cumulative probability density function are respectively in microgrid group：
F (v)=(k/c) (v/c)k-1·exp(-(v/c)k) (1)
F (v)=1-exp (- (v/c)k) (2)
In formula：V is wind speed, and k and c are respectively form parameter and scale parameter；
The probability density function of microgrid group's internal loading and the expression formula of cumulative probability density function are respectively：
In formula：L is load, and μ and σ are respectively the mathematic expectaion and mean square deviation of Gaussian Profile；
N dimension Vine copula functions basic structure be：
In formula：xiFor i-th of variable；For variable xn-1、xnCopula functions, v-jFor except variable xjOuter variable
Set.
In a kind of possible implementation, in step 3, the process bag that variable is run to microgrid group and is sampled
Include following steps：
Step 31, uniform random number w1, w2, w3 are produced in section [0,1]；
Step 32, x1=w1 is made；Bring w2 into formula x2=f-1(w2,x1,ρ1), obtain x2；
Step 33, w2, w3 are brought into formula x3=f-1[f-1(w3,x2,ρ3),x1,p2], obtain x3；
Step 34, (x1, x2, x3) is the random number for meeting to require；
In microgrid group's Reliability evaluation, it is necessary in each microgrid group that samples distributed power source operation conditions and load
Size data.The output data of distributed power source include wind speed and illumination intensity, and load data is then in each microgrid
The size of load.For air speed data and load data, can sample to obtain by the Vine copula functions of foundation.
It is in step 3, for the size of intensity of illumination, then saturating by air of sampling in a kind of possible implementation
Brightness coefficient kt, and then obtain the sample value of intensity of illumination：
ρ1Respectively variable x1With x2Between coefficient correlation；ρ3For variable x2With x3Between coefficient correlation；k、ktuRespectively treat
Sampling atmospheric transparency coefficient and atmospheric transparency coefficient maximum；C is the form factor and scale coefficient of Weibull distribution.
In a kind of possible implementation, in microgrid group's reliability assessment, by distributed power source in the microgrid group that samples,
The running status of load obtains the running status of microgrid group, and then calculates the power supply reliability index of microgrid group, and convergence criterion is
Determine the foundation of microgrid group's reliability index result of calculation degree of accuracy.
In step 4, the convergence situation of microgrid group's reliability index is judged using the convergence of scarce power supply figureofmerit, sentenced
Disconnected foundation is shown below：
β, E (x), ε are respectively convergence parameter, variable x average and convergence.
The present invention judges microgrid group in microgrid group's reliability assessment using ENS (lacking power supply figureofmerit) convergence
The convergence situation of reliability index.
In a kind of possible implementation, in microgrid group's Reliability Evaluation Algorithm, in view of microgrid group's internal loading importance
Difference, it is necessary to consider energy allocation strategy problem under microgrid group's island state.Under island operation state, microgrid cluster is first full
The power demands of sufficient critical load, considering the power demands of non-key load.In steps of 5, the microgrid group reliability is commented
Power supply strategy in estimating includes：
Meet the power demands of critical load in this microgrid first, and calculate microgrid residue power supply capacity；
If microgrid has remaining power supply capacity, the power demands for meeting critical load in other microgrids are considered；
Meet the power demands of non-key load in this microgrid again；
Finally meet the power demands of non-key load in other microgrids.
In a kind of possible implementation, as shown in Fig. 2 in step 6, the reliability for calculating microgrid group's system
Process comprise the following steps：
Step 61, history meteorological data, component reliability parameter, microgrid group configuration data and distribution where microgrid group are inputted
Web frame etc. calculates data；
Step 62, the Spearsman relevance parameters of calculation of wind speed historical data and demand history data, and establish correspondingly
Vine Copula functions；Establish the probability-distribution function of the atmospheric transparency data obtained by history intensity of illumination data；
Step 63, sample microgrid group's running status, based on foundation Vine Copula function models sampling microgrid group in point
The running status of cloth power supply and load；
Step 64, microgrid group's self-energy allocation strategy is determined, calculates each microgrid inscribe load；
Step 65, microgrid group's power supply reliability index, and the convergence of judge index are calculated；Sentence as index reaches convergence
According to then performing next step；Otherwise step 63 is performed；
Step 66, result of calculation is exported.
Advantage of the present embodiment based on Vine Copula functions in terms of random correlation between describing multiple variables, to micro-
Load data in net group in each microgrid inner blower installation site air speed data and each microgrid establishes corresponding mould respectively
Type, and microgrid group's Reliability Evaluation Algorithm is established based on Vine Copula functions；Consider in microgrid group and transported between each microgrid
Random correlation between row variable；And microgrid group Reliability Evaluation Model is applied to microgrid group's Reliability Evaluation Algorithm,
To provide guidance for the power supply of microgrid group high reliability, only microgrid group coordinated operation scheduling does not provide guidance, and ensures
The power supply of microgrid group high reliability.
Illustrate technical scheme below by microgrid group's Reliability evaluation calculated examples are combined.
According to microgrid group's system capacity allocation strategy and operation characteristic, do not consider that microgrid controls dynamic process, calculate microgrid
Group's system power supply reliability, Fig. 3 are the system construction drawing of microgrid group.Table 1 is corresponding equipment dependability parameter and other are related
Parameter, table 2 are distributed power source configuration data in each microgrid of microgrid group's system, and table 3 is each microgrid load data in microgrid group.
Table 1：Equipment dependability parameter
Table 2：Each micro-grid distributed generation configuration data in microgrid group's system
Table 3：Microgrid group's system loading data
Table 4 gives microgrid group's system core load Reliability Evaluation result.Numeral is not consider in table bracket
The reliability assessment result that random correlation obtains between microgrid.From table 4, microgrid critical load supplies after considering random correlation
The reliability assessment result that electric reliability obtains has certain difference compared to conventional model.In addition from result in table 4, by
It is different in distributed power source configuration data in each microgrid, cause in each microgrid critical load power supply reliability not fully one
Sample.As microgrid 4,5 relative to microgrid 1,2,3 has higher power supply reliability.
Table 4：Microgrid group's system core load power supply reliability parameter list
Table 5 gives microgrid group's system general load Reliability Evaluation result.Numeral is not consider in table bracket
The reliability assessment result that random correlation obtains between microgrid.
Table 5：The general load Reliability Evaluation result of microgrid group's system
The present invention is not only applicable to the random correlation models that distributed power source is contributed with payload in microgrid group, also fits
There is the modeling between the variable of random correlative relationship for other, only need to be to corresponding system construction drawing and equivalent system structure
Figure adjusts corresponding to carrying out.
Simply the preferred embodiment of the present invention described above, for those skilled in the art,
Without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications are also regarded as this hair
Bright protection domain.
Claims (9)
1. a kind of more micro-grid system reliability estimation methods in region for considering random correlation, it is characterized in that, comprise the following steps：
Step 1, the random correlation running variable to microgrid group is analyzed；
Step 2, wind speed in microgrid group and load are carried out establishing microgrid group's reliability model；
Step 3, variable is run to microgrid group to be sampled；
Step 4, the convergence situation of microgrid group's reliability index is judged；
Step 5, the power supply strategy in microgrid group's reliability assessment is formulated；
Step 6, the reliability of microgrid group's system is calculated.
2. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 1, it is special
Sign is, in step 1, for n variable xi, the random correlation variable is analyzed using the method for hypothesis testing,
Detailed process comprises the following steps：
Step 11, H is assumed in definition0With alternative hypothesis H1, it is assumed that H0For n variable xiBetween uncorrelated, i.e. correlation coefficient ρ=0；It is standby
Select and assume H1For n variable xiBetween related, i.e. correlation coefficient ρ ≠ 0, index of correlation α=1；
Step 12, the statistic γ between variable is calculatedsAnd by with table look-up to obtain its critical statistics amount
Step 13, determine whether to receive to assume H0：IfThen receive to assume H0, otherwise refuse to assume H0, receive alternative vacation
If H1。
3. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 1, it is special
Sign is in step 2, to carry out establishing microgrid group's reliability mould using wind speed in Vine copula function pair microgrid groups and load
Type, detailed process comprise the following steps：
Step 21, n dimension variable datas x is asked for respectively1,x2,x3,…,xnProbability density function and cumulative probability density function, and
F is designated as respectivelyi(xi) and Fi(xi)；
Step 22, variable data x is asked for respectively1,x2,x3,…,xnBetween Spearsman relative coefficients, and be designated as ρij；
Step 23, to relative coefficient ρijRelevance ranking is carried out according to magnitude relationship；
Step 24, the ranking results based on coefficient correlation, variable is built according to the modeling rule of Vine copula functions
Mould, obtain n dimension variables x1, x2, x3..., xnRandom relevance function expression formula.
4. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 3, it is special
Sign is that the random relevance function expression formula includes：
The probability density function of wind speed and the expression formula of cumulative probability density function are respectively in microgrid group：
F (v)=(k/c) (v/c)k-1·exp(-(v/c)k) (1)
F (v)=1-exp (- (v/c)k) (2)
In formula：V is wind speed, and k and c are respectively form parameter and scale parameter；
The probability density function of microgrid group's internal loading and the expression formula of cumulative probability density function are respectively：
In formula：L is load, and μ and σ are respectively the mathematic expectaion and mean square deviation of Gaussian Profile；
N dimension Vine copula functions basic structure be：
In formula：xiFor i-th of variable；For variable xn-1、xnCopula functions, v-jFor except variable xjOuter variables set
Close.
5. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 1, it is special
Sign is that in step 3, the process being sampled to microgrid group operation variable comprises the following steps：
Step 31, uniform random number w is produced in section [0,1]1、w2、w3；
Step 32, x is made1=w1；By w2Bring formula x into2=f-1(w2,x1,ρ1), obtain x2；
Step 33, by w2、w3Bring formula x into3=f-1[f-1(w3,x2,ρ3),x1,p2], obtain x3；
Step 34, (x1, x2, x3) it is the random number for meeting to require.
6. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 5, it is special
Sign is in step 3, for the size of intensity of illumination, then to pass through atmospheric transparency coefficient k of samplingt, and then obtain intensity of illumination
Sample value：
ρ1Respectively variable x1With x2Between coefficient correlation；ρ3For variable x2With x3Between coefficient correlation；k、ktuRespectively wait to sample
Atmospheric transparency coefficient and atmospheric transparency coefficient maximum；C is the form factor and scale coefficient of Weibull distribution.
7. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 1, it is special
Sign is in step 4, to judge the convergence situation of microgrid group's reliability index using the convergence of scarce power supply figureofmerit, judge
Foundation is shown below：
β, E (x), ε are respectively convergence parameter, variable x average and convergence.
8. a kind of more micro-grid system reliability estimation methods in region for considering random correlation as claimed in claim 1, it is special
Sign is that in steps of 5, the power supply strategy in the microgrid group reliability assessment includes：
Meet the power demands of critical load in this microgrid first, and calculate microgrid residue power supply capacity；
If microgrid has remaining power supply capacity, the power demands for meeting critical load in other microgrids are considered；
Meet the power demands of non-key load in this microgrid again；
Finally meet the power demands of non-key load in other microgrids.
9. a kind of more micro-grid system reliabilities in region of the random correlation of consideration as described in claim 1 to 7 any one are commented
Estimate method, it is characterized in that, in step 6, the process of the reliability for calculating microgrid group's system comprises the following steps：
Step 61, history meteorological data, component reliability parameter, microgrid group configuration data and power distribution network knot where microgrid group are inputted
Structure etc. calculates data；
Step 62, the Spearsman relevance parameters of calculation of wind speed historical data and demand history data, and corresponding to foundation
Vine Copula functions；Establish the probability-distribution function of the atmospheric transparency data obtained by history intensity of illumination data；
Step 63, sample microgrid group's running status, it is distributed in the Vine Copula function models sampling microgrid group based on foundation
The running status of power supply and load；
Step 64, microgrid group's self-energy allocation strategy is determined, calculates each microgrid inscribe load；
Step 65, microgrid group's power supply reliability index, and the convergence of judge index are calculated；As index reaches convergence criterion, then
Perform next step；Otherwise step 63 is performed；
Step 66, result of calculation is exported.
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| US20050019629A1 (en) * | 2003-07-23 | 2005-01-27 | Hitoshi Ikuma | Power supply system, multiple dwelling, and computer program |
| CN104504618A (en) * | 2014-12-29 | 2015-04-08 | 天津大学 | Micro-grid reliability evaluation data sampling method based on pair-copula function |
| CN104914775A (en) * | 2015-06-12 | 2015-09-16 | 华东理工大学 | Multi-modal process fault detection method and system based on vine copula correlation description |
| CN105931134A (en) * | 2016-04-25 | 2016-09-07 | 华北电力大学 | Method for evaluating reliability of system with grid-connected photovoltaic power station |
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| US20050019629A1 (en) * | 2003-07-23 | 2005-01-27 | Hitoshi Ikuma | Power supply system, multiple dwelling, and computer program |
| CN104504618A (en) * | 2014-12-29 | 2015-04-08 | 天津大学 | Micro-grid reliability evaluation data sampling method based on pair-copula function |
| CN104914775A (en) * | 2015-06-12 | 2015-09-16 | 华东理工大学 | Multi-modal process fault detection method and system based on vine copula correlation description |
| CN105931134A (en) * | 2016-04-25 | 2016-09-07 | 华北电力大学 | Method for evaluating reliability of system with grid-connected photovoltaic power station |
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